Building a map of the environment of the vehicle while driving is crucial for different high level applications related to driver assistance systems and the field of autonomous driving. A detailed representation of the free space/occupied space around the vehicle provides the basis for systems like path planning, emergency braking, collision warning, etc. The information about the environment in this 2D map is represented by an occupancy probability of the different cells in this map, e.g. with a cell of size 20 cm×20 cm.
Many systems in this field of research rely on stereo-camera, LIDAR (light detection and ranging) or radar based sensors which are expensive and/or hard to handle. An approach often used in literature is the determination of free space in front of the vehicle by “semantic segmentation”. This approach is image-based and requires a large labeled (with ground truth information) database for the training of a classifier. Often the classifiers are trained on regions with asphalt look like or similar texture. After training related to asphalt it is hard for the classifier to detect lawn as free space. But for the planning of collision avoidance maneuvers the detection of lawn as free space can be lifesaving.